Bio

Ilge Akkaya is a Ph.D. candidate in the Electrical Engineering and Computer Science department at UC Berkeley, working with Prof. Edward A. Lee. She received the B.S. degree in Electrical and Electronics Engineering from Bilkent University, Ankara, Turkey in 2010. During her graduate studies, she explored systems engineering for distributed cyber-physical systems, with a focus on distributed smart grid applications and cooperative mobile robotic control. Her thesis work centers around actor-oriented machine learning interfaces for distributed swarm applications.

Compositional actor-oriented learning and optimization for swarm applications

Compositional actor-oriented learning and optimization for swarm applications

Rapid growth of networked smart sensors today offer unprecedented volumes of continually streaming data, which renders many traditional control and optimization techniques ineffective for designing large-scale applications. The overarching goal of my graduate studies has been enabling seamless composition of distributed dynamic swarm applications. In this regard, I work on developing actor-oriented frameworks for deterministic and compositional heterogeneous system design.

A primary goal of my graduate work is to mitigate the heterogeneity within Internet-of-Things applications by presenting an actor-oriented framework, which enables developing compositional learning and optimization applications that operate on streaming data. Ptolemy Learning, Inference, and Optimization Toolkit (PILOT) achieves this by presenting a library of reusable interfaces to machine learning, control and optimization tasks for distributed systems. A key goal of PILOT is to enable system engineers who are not experts in statistics and machine learning to use the toolkit in order to develop applications that rely on on-line estimation and inference. In this context, we provide domain-specific specializations of general learning and control techniques, including parameter estimation and decoding on Bayesian networks, model-predictive control, and state estimation. Recent and ongoing applications of the framework include cooperative robot control, real-time audio event detection, and constrained reactive machine improvisation.

A second branch of my research aims at maintaining separation-of-concerns in model-based design. In industrial cyber-physical systems, composition of sensors, middleware, computation and communication fabrics yields a highly complex and heterogeneous design flow. Separation-of-concerns becomes a crucial quality in model-based design of such systems. We introduce the aspect-oriented modeling (AOM) paradigm, which addresses this challenge by bridging actor-oriented modeling with aspect-oriented abstractions. AOM specifically enables learning and optimization tasks to become aspects within a complex design flow, while greatly improving scalability and modularity of heterogeneous applications.